Processing UAS Data

Pix4D Review

Overview:

This lab will use Pix4D software to construct a orthomosaic image. Previously, this class had only made georeferenced mosaic imagery. The software Pix4D is the current premier software for constructing point clouds, and is also very easy to use.

Before starting Pix4d, it is important to make sure the images are highly overlapped to create a 3D model that is accurate. The more overlap between images, the more accurate the 3D model will be. More overlap leads to better automatic aerial triangulation which creates a sharper 3D model. If the user if flying over sand or snow the overlap must be at least 85% frontal overlap and at least 70% side overlap. A large percentage of overlap is needed because sand and snow have very little visual content, so each overlapping image can get as much contrast between images as possible. Rapid check is to verify the proper areas and coverage of the data collection. Rapid check processes the data very quickly, but the results have fairly low accuracy.

Pix4d can process multiple flights at once as long as the coordinate system (both horizontal and vertical) of the images is the same. Oblique images can be processed in Pix4d as long as they have good overlap and GCPs. GCPs are not necessary to use Pix4d, but they are highly recommended because they create a much more accurate model. The quality report is used to find the strength and quality of the matches. 

Pix4D Software:  

Dr. Hupy provided the class with UAV imagery from a sand mine south of Eau Claire in order to complete this lab. To start, all images are imported into Pix4d mapper. The area of interest (AOI) is chosen and the flight path can then be visualized. For this lab, a freely drawn polygon was used to create the AOI. After processing the images, the quality report is then created and provides specific details about the images. Figure 1 is the quality report for this lab.

Figure 1: Summary of the quality report

Image 2 is a orthomosaic and the corresponding sparse Digital Surface Model (DSM) before densification created in the report.

Figure 2: Orthomosaic and DSM based off the report

Figure 3 is an image produced by the quality report showing areas of overlap with the images. Areas in green are the areas that have multiple overlapping images. Areas that are red and yellow are areas where overlap is poor. Areas in the middle of the image have more overlap than the edges of the image. As long as the area of interest is an area of high overlap, the output will be of high quality. 

Figure 3: The areas of overlap in the AOI

Final Overview:

This lab introduced how to quickly and easily use pix4D to process UAV data. Pix4D is a great way to visualize 3D data and produce a high quality map. Pix4D can be used by anyone with UAV data to create a map. Overall, this was a great lab to complete to finish class.

Topographic Survey

Introduction:

This lab is intended to teach how to engage in a survey of various point features on campus using a high precision GPS unit. The data will be collected as a  collectively . Each person will take turn with a partner to take a GPS point with the GPS unit. The data gathered should then be turned into continuous interpolated maps. The following interpolation methods should be used: IDW, Kriging, Natural Neighbor, Spline, and TIN.

Study Area:

The study area for this lab is a green patch of grass near the 'Sprites' between the buildings Centennial and Schofield. Figure 1 below is a map of the study area. The data points are seen below between the two academic building. This is a common area where students gather and 'chill' before or after classes.

Figure 1: A map of the study area where the data points were collected

Methods:

The data points were gathered with a survey grade GPS that has sub centimeter accuracy. The GPS records the points though a bluetooth connection. The points were gathered using a random sampling method. The random sampling method is a great method to use because it is an unbiased way to acquire a random sampling of data points. Figure 2 below are the data points that were gathered in the lab.

Figure 2: The data points gathered with the survey grade GPS

The next part of the lab is to use different interpolation methods on the acquired data points.
The following methods are: the following: IDW, Kriging, Natural Neighbor, Spline, and TIN.The inverse distance weighted (IDW) interpolation method is used to predict the elevation of the continuous surface surrounding the data points. The Kriging method generates an estimated elevation of the surfaces surrounding the data points by using the elevation of the data gathered as a reference. The Natural Neighbor method is similar to the methods above except the elevation data used as a reference is taken from data points that are near the area in question. The spline method uses a polynomial algorithm to create the continuous surface model with the elevation points gathered with the class. The last interpolation method used was TIN. A triangulated irregular network (TIN) is a representation of elevations created with triangles calculated with the gathered three-dimensional coordinates.

Results/Discussion:

The first interpolation is seen in figure 3 below. The inverse distance weighted interpolation (IDW) method shows the elevation for a small grass area with a knoll on it. The areas in the left upper portion of the map are areas of high elevation because the points were taken on the knoll above the rest of the grassy area.

Figure 3: The IDW interpolation was used on the data points in the map above

 Figure 4 seen below is a map of the data points using the kriging method. The kriging method shows the elevation for a small grass area with a knoll on it. The higher elevation areas are white or pink in color. The elevation is high in those areas because of the grassy knoll.

Figure 4: The kriging interpolation method was used on the data points in the map above

Figure 5 below is a natural neighbor interpolation map that shows the elevation for a small grass area with a knoll on it. The areas in the left upper portion of the knoll are areas of high elevation because the points were taken on the knoll above the rest of the grassy area.

Figure 5: The natural neighbor interpolation method was used on the data points in the map above

Figure 6 seen below is a map of the data points using the spline method. The spline method shows the elevation for a small grass area with a knoll on it. The higher elevation areas are white or pink in color. The elevation is high in those areas because of the grassy knoll.

Figure 6: The spline interpolation method was used on the data points in the map above

Figure 7 seen below is a map of the data points using the TIN interpolation method. The TIN method shows the elevation using a number of triangles put together creating the small grass area with a knoll on it. The higher elevation areas are the areas with red and orange coloring. The elevation is high in those areas because of the grassy knoll.

Figure 7: The spline interpolation method was used on the data points in the map above

A few weeks ago, the lab was to create interpolation maps of stratified sampling. This week, we used random sampling. The stratified sampling created a much more realistic representation of the area being surveyed versus the random sampling we just did in this lab. The random sampling interpolations created maps that were not very specific to the terrain we gathered GPS data points on.

Conclusion:

Upon the conclusion of this lab, it was clear that the stratified sampling method is a better data gathering method than then random sampling method. The stratified sampling method was used in a previous lab for this class. If this lab were to be conducted again in the future, a word of advice would be to spread out the data points in the area of interest. Another word of advice would be to make the interpolated maps somewhat translucent to see the area in which the data point was taken. Overall, this lab was interesting and informative which made the interpolation process enjoyable.

Arc Collector

Introduction:

This lab is designed to continue developing the skills gained in the last lab with ArcCollector. The use of cellphones is nearly universal, so ESRI decided to cash in on the cellphone fad by making an app that allows for data gathering with the touch of a thumb. In the previous lab, we used a database that was set up prior to use. In this lab, each student created his or her own database with their own question in mind. Since each person has their own database, it is critical the domains are set up correctly. This specific lab is analyzing the temperature at ground level, temperature at 2 ft in the air, type of ground, and whether or not it needs an upgrade from the UWEC grounds-crew. The completion of this lab depends on a successful database set-up, smooth data gathering with the kestrel thermometer, and ArcMap on the desktop computer to create the final product.


Study Area:

The study area for this lab is nearly the same as it was in the previous lab. The UW-Eau Claire campus on the South side of the Chippewa River is the study area for this lab. Figure 1 below is a map of the area of interest. UWEC's main campus is the study area for this lab. The data was collected on 11/29/16 at around 11AM, so the temperature had not peaked for the day. The high for the day was 46 degrees Fahrenheit.

Figure 1: The study area of this lab is the UWEC campus

Methods:

In order to set up the online database, there were a few requirements for this lab. One was the database required three fields for attributes, one text field for notes, a floating point or integer, and one of the persons choice. The different domains help normalize the data in the field to make data collection enjoyable instead of frustrating. Figure 2 below is the domains used for this lab.

Figure 2: The domain for the database

The domains for this lab are: ground condition, ground cover, notes, temperature at 2 feet above the ground, and the temperature at ground level. The temperature domains required a long integer field type. The notes field is text and the ground condition and cover are both coded values. This means the one of the coded valued must be selected.

Once all of the domains were set in place, a test point was used in the corner to make sure there were no errors in the set up--which there was not, so the data gathering was the next thing to do. 20 data points were gathered around the UWEC campus. Once the data was collected, it was brought into ArcMap to create the continuous model of the temperatures at two different heights.

Results/Discussion:

Figure 3 below is the final map of the temperature in Fahrenheit at ground level. Most of the data points were between 39 and 41 degrees Fahrenheit.

Figure 3: A map of the temperature at ground level

Figure 4 below is the final map of the temperature in Fahrenheit at 2 feet above the ground. Most of the data points were between 37 and 38. This is nearly two degrees less than the ground level temperatures.

Figure 4: A map of the temperature at 2 feet above ground level

The results of this particular lab were slightly different than initially thought. The temperature was overall colder 2 feet in the air whereas the ground level temperatures were all warmer. This could be because the ground is still in the process of freezing for winter, or because the larger the distance away from the ground, the colder the air will be. Unfortunately, the colder temperatures caused for a shortened data gathering time, so only 20 data points were collected in total. Overall, this lab proved to be a tough challenge, yet rewarding at the same time. The link below is a interactive map on ArcCollector with the data points collected. (http://arcg.is/2g4mCMD)

Micro-climates at UWEC

Introduction:

Cell phones often times have a higher computing speed and power than most GPS units, so it is a reliable option to use online data to aid in data collection. Arc collector is an app that allows data collection online from a cell phone or tablet. This opens doors for gathering data in places that was once difficult. As an entire class, we split up with a partner and went to the assigned zone. Once within the zone, you and your partner could start taking GPS points anywhere you wanted. We gathered 175 data points total as a class. As we were gathering data, we could see other groups data points pop up on our own maps. This is the exact reason why Arc Collector is such a useful tool. Many people can access and gather data in real time while being together or many miles apart.


Study Area:

The University of Wisconsin Eau Claire's campus was broken down into 7 different zones. Two pairs of two went to each zone. The zone that we were assigned to was zone 1. Figure 1 below is the map of campus broken up into zones.

Figure 1: Campus split up into 7 zones 

Zone 1 is the blue highlighted area on the map above. The area also included the walking bridge, Haas academic building, and two large parking lots near the Haas and HSS academic buildings.


Methods:

Before we could gather data points, we needed to download Arc Collector from the app store in order to connect our devices to ArcGIS online. ArcGIS online makes it possible to run the software through devices such as a cell phone, or anything with a high processing system. After we connected to ArcGIS online, we went over the attribute data that was going to be collected in the field. The measurements we were taking at each point were the temperature, wind speed, wind direction, and dew point.

Once we reached zone 1, we decided to take our first point in the middle of the walking bridge on campus. We took out our handy Kestrel thermometer to gather the necessary data. We took the temperature and dew point as well as the wind speed and direction. Figure 2 below is a picture taken while recording the third data point.

Figure 2: A photo taken at a data point

We recorded 11 data points in zone 1. After all groups had finished collecting their data, we could all look at our individual maps, but they all had the same exact data. This is a great way to keep data normalized. Figure 3 below is a map of all the data points collected by the class.

Figure 3: Data points collected by the entire class

Figure 4 below is the attribute table for all of the data points located in figure 3 above. The four columns that were of main interest were: TP, DP, WS, and WD.

Figure 4: Attribute table for the classes data

Since all of the data points are together, we could make several maps of the four micro-climates on campus. For all of the following maps, I created a continuous surface feature to show the interpolated average of each of the attributes. I used the inverse distance weighted (IDW) interpolation method on all of the maps. The IDW interpolation method estimates cell values by averaging the values of sample data points in the near area of each processing cell. Figure 5 below is the map created with temperature data.

Figure 5: A map of temperature across the UWEC campus

The temperature was gathered in Fahrenheit for this lab, so all map with temperature will be in Fahrenheit. Figure 6 below is a map of the dew point across UWEC.

Figure 6: A map of dew point across the UWEC campus

The dew point is a measure of the temperature air has to be to condense and form dew. Figure 7 below is a map of the wind speed on campus.

Figure 7: A map of wind speed across the UWEC campus

The wind speed was measured in miles per hour for this lab. Figure 8 below is a map of the wind direction while taking the data points.

Figure 8: A map of wind direction across the UWEC campus

The direction the wind was coming from was recorded along with the rest of the attribute data for each data point. I chose to keep the wind speed in the map to show which ways the wind was blowing very strong versus not very strong. I made all of the continuous surface features 15% transparency on each map to give an idea of where each data point is located.


Results:

Each map above is different from each other, yet they have everything in common. The wind speed map is particularly interesting in that the wind speed was highest on the middle of the walking bridge. There is always a lot of wind when walking over the bridge, so the map was not surprising, yet it still interesting. I originally did not have my maps with a 15% transparency, and I am very glad I went back to change that. The transparency of the continuous layer makes it easier to see where the data points were taken. The temperature is nearly even across the map, and that could be because the sun was shining and it was a very nice day out. The one interesting area on the temperature map was an area that is heavily wooded. That area was much colder than the rest of campus most likely due to the fact that the sun was not shining on that area. The dew point was higher in areas of more populated areas such as the Davies parking lot and the back of Davies area. The dew point was much lower in areas where things were more spread out and there were less people. The wind direction was all over the place, so this could be because of human error, or the wind was blowing in many directions while we were gathering data. The possibilities for both options are very likely, so there is no definite answer to why the wind was blowing in so many directions.

Conclusion:

This lab exercise allowed me to gain knowledge about a new way to collectively gather data. Arc Collector had opened many doors of opportunities in the field. Arc Collector was very effective in that the entire class was able to create a set of points with normalized data without any problems. It would be interesting to look at micro-climates across UWEC in more detail and with more fineness. There may have been some patterns in the data that I missed, but for the most part, this lab was definitely a success. Arc Collector did its job of putting together all of the data gathered, and we were able to successfully analyze four different types of micro-climates at UWEC.

Navigating the Priory with a Map and Compass

Introduction: 

The purpose of this lab was to use the maps we created last week in class to navigate the terrain to find points behind the Priory. The Priory is a UW-EC owned building about 5 miles away form campus. I was part of group two with two other people. For this lab, we could only use one persons maps we created last week. For this lab, we ended up using the maps I created as well as a compass to find five points behind the Priory. We brought a GPS along with us to track our path. We were given five points to find in UTM meter form. 

Methods: 

After meeting at the Priory on November 2nd 2016, we had to approximate where the five points we needed to find were on the map. The ticks on the map were in 50 meter increments that helped approximate where the point would be out in the field. Figure 1 below is a picture of the five coordinates we needed to find. 

Figure 1: Five coordinates group 2 needed to find

Once we knew where we were headed, we needed to figure out our pace count for 100 meters. My pace count was 76 paces per 100 feet. The pace count is used when moving towards a distant object when direction is not clear. In this case, we had to walk in the woods and find our points, so using a pace count to track our distances was helpful. We also used a Trimble Juno 3B GPS as seen in figure 2 below to track our path to our points. 

Figure 2: The GPS group 2 used to find the 5 points on the paper

In order to find the coordinates we were looking for, we used the compass to find the angle degree we were going to walk in. We pointed the north arrow of the compass north and lined up the compass in a straightedge to the point. This gave us a working "compass" to the point. We needed to do this from each point to the next. We decided to go to point 1 first, point 5 second, point 4 third, point 2 fourth, and point 2 fifth. In order to use the compass, we had to adjust the reading for each point. Then, we had to hold the compass at chest level to read the direction we had to walk. The person holding the compass stayed in one place and another person counted their paces to a tree in the compasses path and stayed there. We repeated this process over and over again until we found all our points. 

Results:

Group 2 took a rough path to our first point. Figure 3 below is a map of the track the GPS captured. 

Figure 3: A map of the GPS tracked as we were finding the five data points

It took a while to get used to finding the coordinate points. We took a heavily wooded path to the first data point labeled '1' on the map above. We then went to point '5' but we took a walking path to avoid going back into the dense brush. Then we went to point 4 which was close to point 5 and it was relatively easy to find. Then we went to point 3 and finished with point 2. 

Figure 4 below is a map of the GPS path of all of six groups in the class. The pink dots on the map represent all possible coordinate points given to the entire class. It is clear that all of the paths captured by the GPS were not straight lines and it appears that we all ran into some type of problem that took the group off course. Sometimes we were slightly thrown off because a large tree or massive brush pile was in our way. We had to move around nature and figure out how to overcome problems regarding paths.

Figure 4: A map of the GPS tracked paths for all six groups in the class

Conclusion: 

This lab featured using a map with a grid system to find five points in the Priory that were given to us as coordinate points. All we could use was our maps, a GPS for tracking, and a compass. The overall execution of this lab was definitely attainable and really taught me how to reach a destination with coordinates only.  

Using a 50 meter UTM grid was an okay measurement for this lab. The spacing was a little far apart, but it worked out in our favor nonetheless. This lab made it clear that straight lines from one point to another are nearly impossible because of the surrounding landscape. From an aerial view, the Priory does not seem like tough terrain to hike, but elevation changes as well as dense forests caused a bit of a struggle for group 2. 

Development of a Field Navigation Map

Introduction:

In order to navigate around the Priory (the study area), we need to know some type of location system, coordinate system, and some type of projection. The issue is that coordinate systems can confuse people depending on what scale they are working with. For example, State Plane and UTM are two popular coordinate systems, yet they are very different. UTM is measured in meters whereas State Plane is measured in decimal degrees. UTM is a popular coordinate system because it is universal and can be used anywhere. A projected coordinate system provides various mechanisms to project maps of the earth's spherical surface onto a two-dimensional Cartesian coordinate plane. A geographic coordinate systems use latitude and longitude. A UTM is more accurate because it is a coordinate system altered to better fit the area of interest. For this lab, we will be creating two maps for navigation around the Priory, one that utilizes the UTM coordinate system and another coordinate system that uses decimal degrees.

 

Methods:

Before we created our maps in ArcGIS, we formatted the paper so the dimensions were 11X17 and the paper was in landscape format. The maps had to include the following: north arrow, a scale bar, what projection it is in, the coordinate system of the map, a labeled grid, data sources, a background, and a watermark.

Results:

In order to create a map that is useful in helping find points via GPS points, a grid drawn across the background image is very important. Figure 1 below is a map of the UWEC Priory. The coordinate system is UTM, so the grid is measured in meters. It is clear that the grid is meters because there are no coordinates, but rather measurements by meters. After placing the grid, I used the contour tool located under raster surface in ArcMap to create 25 and 10 meter contour lines.

Figure 1: A map of the Priory using the UTM coordinate system

 Figure2 below is the same map as above, except is in a geographic coordinate system that uses decimal degrees. This is where the latitude and longitude come into play. They are used instead of measurements around the grid. I used the same contour lines in figure 1 as I did in figure 2.

Figure 2: A map of the Priory using a coordinate system that uses decimal degrees

Conclusion:

The two maps created for this lab required a lot of initial thought process to create a usable map. This was the first time I created a map that I will use in the field. Many people have issues with coordinate systems depending on what scale they are working with, and it can really confuse people. I think that the UTM map will be easier to read, but I will find out on Wednesday. Overall, creating these maps has really clarified the use of coordinate systems versus latitude and longitude for me. I am excited to see how it goes in the field.

Lab 6

Lab 6

Introduction:

The purpose of this lab is to conduct a survey with a grid based coordinate system. The techniques learned in this lab are to be used when 'technology' is not readily available or usable. It is important to be able to follow through with a survey no matter the measurement tools at hand. For this lab, we are to preform a survey in Putnam Park. The survey is to be conducted by using distance and azimuth. This method is a very basic survey technique, and is similar to the point-quarter method and mapping out linear features on the landscape. The distance and azimuth method uses a handheld compass and a handheld rangefinder. While out in the field, we also learned about the following survey equipment: a GPS, tape reel, and a sonic distance finder.

Study Area:

The study area for this lab is Putnam Park Drive. We were on the gravel path with our backs to the ridge looking out into the swampy marsh area. We recorded the coordinates for one place and took each measurement from that exact spot. The coordinates for each point of origin (there were three points for the entire class) were recorded and shared throughout the class. Each point of origin had the distance and azimuth for ten different trees in Putnam Park. 

Methods:

After choosing the point where we were going to take our points from, we needed to retrieve the coordinates of the point of origin. The Bad Elf GPS gave us the coordinate point and we recorded it in our field notebooks. All of the groups used this GPS to attain their point of origin. We then proceeded to record the distances of the trees to the point of origin with the laser targeting range finder II. We then recorded the azimuth with the Suunto compass. The compass was previously adjusted 1 degree for declination. Figure 1 below shows two of my colleagues using both of the measuring tools we used to gather our information.

Figure 1: Colleague 1 on the left using the Laser to find the distance,
and colleague 2 on the right using the compass to find the azimuth 

 The laser gave the distance in meters of how far the tree was from the point of origin. The compass gave the azimuth of the tree in regarding its angle to the point of origin. We also recorded two other attributes along with distance and azimuth. We recorded the diameter of the tree as well as the type of tree. Figure 2 below shows another one of my colleagues reading the tape reel and recording the diameter of the tree in centimeters.

Figure 2: Colleague 3 using the tape reel to measure the diameter of the tree trunk

Once all of the data were recorded, we entered the data into a spreadsheet everyone could access. From there I took the data and normalized it. Once the data was normalized it looked like figure 3 below. 

Figure 3: The normalized data from the tree survey

The table above is the final excel file before it was imported into a GIS. The goal of using the GIS is to create a digital survey map. In order to do this, we had to use the 'Bearing Distance to Line' tool in ArcGIS. The tool created lines extending from the point of origin. This is an extremely helpful tool to visually show the distances of points from the point of origin. Figure 4 below shows the lines representing the distance from the point of origin to the tree. 

Figure 4: The 'Bearing Distance' tool created the lines from the point of origin

The bearing distance tool is helpful in showing the distance of the trees to the point of origin. Figure 5 below represents the vertices of each tree point.

Figure 5: The 'Features Vertices to Points' tool creates points at the end of each distance line

In order to create the points, the tool 'Features Vertices to Points' had to be used. The tool is located under the data management tools in the toolbox in ArcMap. It essentially creates a point where each tree, or whatever is being recorded, is located. The tool just creates a point at each vertex of every distance line.

Results/Discussion:

After creating a digital image in a GIS, the distances and azimuths for each tree seemed to differ greatly. I would not suggest this method of retrieving data to anyone who wants an accurate data set. The readings of the distance finder and azimuth had some large differences, and this is because of human error. All six group members took each type of measurement and that in and of itself results in error. There is also the fact that we are different heights and we were not standing on the exact point of origin 100% of the time. After we recorded out point of origins coordinates, we went to use the sonic distance finder to survey the trees. The sonic distance finder did not work for my group, so we had to revert to using the laser targeting range finder II. Technological difficulties occur even when the survey equipment seems unbreakable. This particular solution was solved by using a different distance measuring tool, the laser targeting range finder II. All of these tools are accurate enough to retrieve points and data that is close to the actual numeric value. 

Conclusions:

If you know the exact point of origin down to the coordinates, it is possible to use the distance azimuth surveying method to attain data even though a GPS is not at hand. The better the equipment, the more accurate the data results will be. If this survey was to be recreated, I would go with the point quarter survey. The point quarter survey takes random survey points in a measured out grid with four large quadrants. I believe it is crucial to know how to use the distance azimuth survey method for future endeavors when the use of technology is not permitted or accessible.